Lead ML/AI Platform Engineer

Job not on LinkedIn

🔥 0 minutes ago

🇵🇱 Poland – Remote

⏳ Contract/Temporary

🟠 Senior

🏗️ Platform Engineer

👻 Ghost score 10%

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SavvyMoney

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2009

🏦 Banking

💳 Fintech

☁️ SaaS

🔥 Funding within the last year

💰 $225M Private Equity Round - SavvyMoney on 2025-10

Banking • Fintech • SaaS

SavvyMoney is a financial wellness and embedded fintech platform built for banks, credit unions, and fintechs. The company provides real-time credit score insights, monitoring and alerts, personalized pre-qualified offers, account opening and onboarding workflows, and analytics to drive deposit and loan growth. SavvyMoney integrates with core and digital banking systems (70+ integrations), is positioned as a B2B SaaS partner to financial institutions, and is used to increase engagement, personalization, and measurable ROI—reaching tens of millions of consumers.

📋 Description

• Own the ML/AI platform from experimentation to production, including training infrastructure, model serving, inference pipelines, and production integration • Partner with the Data Platform Architect to set company-wide AI/ML technical direction, architecture, tooling, standards, and build-vs-buy decisions • Lead feature engineering, model training, model registry, and hosted inference in Amazon SageMaker • Drive GenAI/LLM usage, fine-tuning, and agentic workflows in Amazon Bedrock and AgentCore • Build feedback and data pipelines using AWS Glue, Lambda, and Step Functions • Own the serving layer and integrate ML services with Java microservices, including API contracts and latency/throughput trade-offs • Drive retrieval architectures, evaluation harnesses, serving patterns, and solution selection for GenAI/LLM initiatives • Develop MCP and agent workflow patterns, stateless/stateful designs, and responsible guardrails for regulated environments • Partner with the Data Scientist to productionize models and establish feature pipelines and serving infrastructure • Work with product, data, and engineering leadership to identify high-impact ML opportunities and translate them into roadmaps • Represent the AI/ML function in cross-functional forums and communicate technical trade-offs • Collaborate with DevOps on ML-specific CI/CD and observability extensions • Define contracts and own the serving side for backend Java microservices

🎯 Requirements

• 8+ years in software or ML engineering, including 5+ years shipping production ML systems • Track record of owning ambiguous, high-scope problems end to end • Demonstrated technical leadership, including shaping ML strategy and mentoring senior engineers • Hands-on experience with Amazon SageMaker, Amazon Bedrock, and AgentCore • Experience with JupyterLab, Spark, and MLflow • Deep working knowledge of AWS S3, Athena, Redshift, Glue, Step Functions, and Lambda • Strong SQL skills for analytical and ML workloads • Production experience with GenAI/LLMs, RAG, prompt engineering, and evaluation • Familiarity with vector databases such as pgvector or Pinecone • Working knowledge of Java for reviewing service code, defining API contracts, and debugging integrations • Deep expertise in Python, scikit-learn, pandas, NumPy, PyTorch and/or TensorFlow, and XGBoost / LightGBM • Solid MLOps fundamentals, including model monitoring, drift detection, reproducibility, experiment tracking, model registry, and cost observability • Excellent written and verbal communication • Strong collaboration skills • Ability to operate as an independent contractor through own entity or approved contracting arrangement • Reliable overlap with US Pacific business hours • Nice to have: ClickHouse or comparable columnar/real-time analytical database experience • Nice to have: Fine-tuning experience such as LoRA, QLoRA, instruction tuning, or RLHF • Nice to have: Streaming and real-time inference experience with Kafka or Kinesis • Nice to have: Infrastructure-as-code with Terraform, AWS CDK, or CloudFormation • Nice to have: Experience operating ML systems at meaningful scale • Nice to have: Open-source contributions, conference talks, papers, or patents in ML/applied ML

🏖️ Benefits

• Equity Compensation Package • Flexible Time Off (FTO) • Medical, Dental, Vision – 100% premium paid for employee • Disability/Life Insurance • Opportunity for learning and career growth • Reimbursement for remote work setup • Monthly stipend for phone and internet • Team building events, culture activities, all hands events • Paid time off to volunteer and serve the community • Half day Fridays • 401k matching contribution